Immunohistochemistry (IHC) refers to the process of detecting proteins in cells of a tissue section. IHC staining is widely used in the diagnosis of abnormal cells such as those found in cancerous tumors. Common practice in pathology laboratories is to score IHC-stained images. By indicating a tumor is negative or positive, the percentage of positively stained tumor cell nuclei is able to be reported, which is able to assist pathologists for the final scoring purpose.
Some research has been done for the percentage estimation of positively stained tumor cell nuclei. The goal is achieved by using a color de-convolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. The quantitative results are calibrated using cell counts defined visually as the gold standard.
Most of the nuclei area estimation algorithms require a user to manually specify a cut-off threshold value for defining positive/negative. Although this type of user interaction is allowed, it is able to be improved.
However, most of the existing research work is performing nuclei area estimation, which is fast in speed but does not provide nuclei number estimation. According to pathologists, number information is a plus and is able to be provide extra hints when scoring IHC-stained images.
Nuclei's shapes and image intensities vary significantly. Touching cases (e.g., when nuclei are connected with each other) makes the number estimation even more challenging. Under-estimation and over-estimation are two major issues when developing automated nuclei number estimation.